Flow cytometry relies on the measurement of signals from a combination of fluorescent molecules, or fluorescence-labeled reagents, to indicate the presence or amount of a single species of target molecule in a sample preparation. The fluorescence spectra of these fluorescent molecules or reagents frequently overlap and, as a result, it is necessary to apply computational methods to resolve the amount of fluorescence detected for each component of the reaction mixture. The most common protocols use a series of “single-stain” samples that individually provide independent measures of the fluorescence emitted by each of the fluorescent molecules, and a “matched series” of measurements for (putatively) non-fluorescent objects either present in the “single-chain” sample or independently obtained as non-stained objects. The slopes of the lines connecting the median (or mean) fluorescence values for the negative and positive measurement groups measured independently for each fluorescence is then used to estimate the amounts of overlap fluorescence that must be subtracted from a value observed when all of the fluorescent reagents are used together.
Fluorescence
Fluorescence is a cyclical process where a luminescence is generated by certain molecules in which the molecular absorption of a photon triggers the emission of another photon with a longer wavelength.
In the fluorescence process, certain molecules are capable of being excited, via absorption of light energy, to a higher energy state, also called an excited state. The energy of this short-lived excited state decays (or decreases) resulting in the emission of light energy. The emission of light via this process is to “fluoresce.”
A fluorophore is a molecule that is capable of fluorescing. In its ground state, the fluorophore molecule is in a relatively low-energy, stable configuration, and it does not fluoresce. When light from an external source hits a fluorophore molecule, the molecule can absorb the light energy. If the energy absorbed is sufficient, the molecule reaches an excited state (high energy); this process is known as excitation. There are multiple excited states or energy levels that the fluorophore can attain, depending on the wavelength and energy of the external light source. Since the fluorophore is unstable at high-energy configurations, it eventually adopts the lowest-energy excited state, which is semi-stable. The excited lifetime (the length of time that the fluorophore is an excited state) is very short; the fluorophore rearranges from the semi-stable excited state back to the ground state, and part of the excess energy may be released and emitted as light. The emitted light is of lower energy, and of longer wavelength, than the absorbed light, thus the color of the light that is emitted is different from the color of the light that has been absorbed. De-excitation returns the fluorophore to its ground state. The fluorophore can absorb light energy again and go through the excited state to ground state process repeatedly.
Fluorescence Spectra
A fluorescent dye absorbs light over a range of wavelengths and every dye has a characteristic excitation range. This range of excitation wavelengths is referred to as the fluorescence excitation spectrum and reflects the range of possible excited states that the dye can achieve. Certain wavelengths within this range are more effective for excitation than other wavelengths. A fluorophore is excited most efficiently by light of a particular wavelength. This wavelength is the excitation maximum for the fluorophore. Less efficient excitation can occur at wavelengths near the excitation maximum; however, the intensity of the emitted fluorescence is reduced. Although illumination at the excitation maximum of the fluorophore produces the greatest fluorescence output, illumination at lower or higher wavelengths affects only the intensity of the emitted light; the range and overall shape of the emission profile are unchanged.
Fluorophore molecules, when excited, emit over a range of wavelengths. This range of wavelengths is referred to as the fluorescence emission spectrum. There is a spectrum of energy changes associated with these emission events. A molecule may emit at a different wavelength with each excitation event because of changes that can occur during the excited lifetime, but each emission will be within the fluorescence emission spectrum. Although the fluorophore molecules all emit the same intensity of light, the wavelengths, and therefore the colors of the emitted light, are not homogeneous. The emission maximum is the wavelength where the population of molecules fluoresces most intensely. The excited fluorophore also can emit light at wavelengths near the emission maximum. However, this light will be less intense.
The emission maximum for a given fluorophore is always at a longer wavelength (lower energy) than the excitation maximum. This difference between the excitation and emission maxima is called the Stokes shift. The magnitude of the Stokes shift is determined by the electronic structure of the fluorophore, and is characteristic of the fluorophore molecule. The Stokes shift is due to the fact that some of the energy of the excited fluorophore is lost through molecular vibrations that occur during the brief lifetime of the molecule's excited state. This energy is dissipated as heat to surrounding solvent molecules as they collide with the excited fluorophore.
Filters and Light Sources
Fluorescence requires a source of excitation energy. There are many light source options for fluorescence. Selecting the appropriate light source, and filters for both excitation and emission, can increase the sensitivity of signal detection.
Several types of light sources are used to excite fluorescent dyes. The most common sources used are broadband sources, such as, for example, mercury-arc and tungsten-halogen lamps. These lamps produce white light that has peaks of varying intensity across the spectrum. When using broadband white light sources it is necessary to filter the desired wavelengths needed for excitation; this is most often done using optical filters. Optical filters selectively allow light of certain wavelengths to pass while blocking out undesirable wavelengths. A bandpass excitation filter transmits a narrow range of wavelengths and may be used for selective excitation.
Laser excitation sources provide wavelength peaks that are well-defined, selective, and of high intensity allowing more selective illumination of the sample. The best performance is achieved when the dye's peak excitation wavelength is close to the wavelength of the laser. Several lasers commonly used include, for example, the compact violet 405 nm laser, 488 nm blue-green argon-ion laser, 543 nm helium-neon green laser, and 633 nm helium-neon red laser. Mixed-gas lasers such as, for example, the krypton-argon laser, can output multiple laser lines which may require optical filters to achieve selective excitation. High-output light-emitting diodes (LEDs) provide selective wavelengths, low cost and energy consumption, and long lifetime. Single-color LEDs are ideal for low-cost instrumentation where they can be combined with simple long pass filters that block the LED excitation and allows the transmission of the dye signal. However, the range of wavelengths emitted from each LED is still relatively broad and also may require the use of a filter to narrow the bandwidth.
Filters are important for selecting excitation wavelengths and for isolating the fluorescence emission emanating from the dye of interest. Stray light arising from sources other than the emitting fluorophores (for example, from the excitation source) interferes with the detection of the fluorescence emission. Stray light therefore must be contained to ensure only the fluorescence of the sample registers with the instrument's light-sensitive detectors. When a single dye is used, a long pass emission filter which selectively blocks out the excitation light to reduce background noise may be used to maximize the signal collected. If multiple dyes are used in the sample, a band pass emission filter can be used to isolate the emission from each dye.
Flow Cytometry
Flow cytometry is a technique for counting, examining, and sorting microscopic particles suspended in a stream of fluid. It allows simultaneous multi-parametric analysis of the physical and/or chemical characteristics of single cells flowing through an optical and/or electronic detection apparatus.
Flow cytometry utilizes a beam of light (usually laser light) of a single wavelength that is directed onto a hydro-dynamically focused stream of fluid. A number of detectors are aimed at the point where the stream passes through the light beam; one in line with the light beam (Forward Scatter or FSC) and several perpendicular to it (Side Scatter (SSC) and one or more fluorescent detectors). Each suspended particle passing through the beam scatters the light in some way, and fluorescent chemicals found in the particle or attached to the particle may be excited into emitting light at a lower frequency than the light source. This combination of scattered and fluorescent light is picked up by the detectors, and by analyzing fluctuations in brightness at each detector (usually one for each fluorescent emission peak) it then is possible to derive various types of information about the physical and chemical structure of each individual particle. FSC correlates with the cell volume and SSC depends on the inner complexity of the particle (i.e. shape of the nucleus, the amount and type of cytoplasmic granules or the membrane roughness).
Flow Cytometers
Flow cytometers are able to provide real-time analysis of several thousand particles every second and can separate and isolate particles having specified properties actively. Single-cell suspensions first must be prepared to analyze solid tissues.
A flow cytometer has five main components: 1) a flow cell where a liquid stream (sheath fluid) carries and aligns the cells so that they pass single file through the light beam for sensing; 2) a light source, such as lamps (mercury, xenon); high power water-cooled lasers (argon, krypton, dye laser); low power air-cooled lasers (argon (488 nm), red-HeNe (633 nm), green-HeNe, HeCd (UV)); or diode lasers (blue, green, red, violet); 3) a detector and Analogue to Digital Conversion (ADC) system for generating FSC and SSC as well as fluorescence signals; 4) an amplification system (linear or logarithmic); and 5) a computer for analysis of the signals.
Early flow cytometers were generally experimental devices, but recent technological advances have created a considerable market for the instrumentation, the reagents used, such as, for example, fluorescently-labeled antibodies, and analysis software. Modern instruments usually have multiple lasers and fluorescence detectors; up to 4 lasers and 18 fluorescence detectors within a single instrument are available. Increasing the number of lasers and detectors allows for multiple antibody labeling, and can identify a target population by its phenotype. Certain instruments can take digital images of individual cells more precisely, allowing for the analysis of fluorescent signal location within or on the surface of cells.
The use of fluorescent molecules, such as fluorophore-labeled antibodies, in flow cytometry is a common way to study cellular characteristics. Within these types of experiments, a labeled antibody is added to the cell sample. The antibody then binds to a specific molecule on the cell surface or inside the cell. Finally, when the laser light of the appropriate wavelength strikes the fluorophore, a fluorescent signal is emitted and detected by the flow cytometer.
The data generated by flow cytometers can be plotted in a single dimension, to produce a histogram, or in two dimensional dot plots or even in three dimensions. The regions on these plots can be separated sequentially, based on fluorescence intensity, by creating a series of subset extractions (referred to as “gates”). Specific gating protocols exist for diagnostic and clinical purposes especially in relation to hematology. The plots often are made on logarithmic scales. Signals at the detectors have to be compensated electronically as well as computationally due to emission spectra overlap of different fluorophores. Data accumulated using the flow cytometer may be exported to be re-analyzed elsewhere, freeing up the instrument for other researchers to use.
Fluorescence Activated Cell Sorting (FACS)
Fluorescence-activated cell sorting (FACS) is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of biological cells into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell. It provides fast, objective and quantitative recording of fluorescent signals from individual cells as well as physical separation of cells of particular interest. The term “FACS” is not a generic term for flow cytometry, although many immunologists inappropriately use the term FACS for all types of sorting and non-sorting applications.
Utilizing FACS, a cell suspension is entrained in the center of a narrow, rapidly flowing stream of liquid. The flow is arranged so that there is a large separation between cells relative to their diameter. A vibrating mechanism causes the stream of cells to break into individual droplets. The system is adjusted so that there is a low probability of more than one cell being in a droplet. Before the stream breaks into droplets the flow passes through a fluorescence measuring station where the fluorescent character of interest of each cell is measured. An electrical charging ring or plane is placed just at the point where the stream breaks into droplets. A charge is placed on the ring based on the prior light scatter and fluorescence intensity measurements, and the opposite charge is trapped on the droplet as it breaks from the stream. The charged droplets then fall through an electrostatic deflection system that diverts droplets into containers based upon their charge. In some systems the charge is applied directly to the stream while a nearby plane or ring is held at ground potential and the droplet breaking off retains charge of the same sign as the stream. The stream then is returned to neutral after the droplet breaks off.
Fluorescence Detection
For proper data interpretation, the fluorescent light recorded from one fluorescent source must be distinguished from that recorded from other fluorescent sources. For that reason, the ideal fluorophore has a fluorescence emission profile of a very intense, narrow peak that is well separated from all other emission peaks. Typical organic dyes and fluorescent proteins, however, have broad emission peaks that may overlap, (i.e., emit some light in the same wavelength range). This spectral overlap may compromise data and analysis.
Multiple Fluorescent Signals
Background fluorescence, which may originate from endogenous sample constituents (autofluorescence) or from unbound or nonspecifically bound reagents, may compromise fluorescence detection severely. Briefly, excitation (EX) in overlapping absorption bands A1 and A2 produces two fluorescent species with spectra E1 and E2. The detection of autofluorescence (i.e., the A2-E2 spectra) can be minimized either by selecting filters that reduce the transmission of E2 relative to E1 or by selecting reagents that absorb and emit at longer wavelengths. Although narrowing the fluorescence detection bandwidth increases the resolution of E1 and E2, it also compromises the overall fluorescence intensity detected. Signal distortion caused by autofluorescence of cells, tissues and biological fluids is minimized most readily by using reagents that can be excited at >500 nm. At longer wavelengths, light scattering by dense media such as tissues is much reduced, resulting in greater penetration of the excitation light. The use of optical filters isolate quantitative emission signals S1 and S2.
Multicolor labeling incorporates the use of two or more probes to monitor simultaneously different biochemical functions. This technique has major applications in flow cytometry, DNA sequencing, fluorescence in situ hybridization (FISH) and fluorescence microscopy. Signal isolation and data analysis are facilitated by maximizing the spectral separation of the multiple emissions (E1 and E2). Consequently, fluorophores with narrow spectral bandwidths, such as, for example, Alexa Fluor dyes and BODIPY dyes (Molecular Probes, Eugene, Oreg.), are useful in multicolor applications. An ideal combination of dyes for multicolor labeling would exhibit strong absorption at a coincident excitation wavelength and well-separated emission spectra. Unfortunately, it is not simple to find single dyes with the requisite combination of a large extinction coefficient for absorption (meaning a parameter defining how strongly a substance absorbs light at a given wavelength, expressed per mass unit or per molar concentration) and a large Stokes shift (meaning the difference (in wavelength or frequency units) between positions of the band maxima of the absorption and emission spectra of the same electronic transition, see infra).
Signal Amplification
Fluorescence signals may be amplified by increasing the number of fluorophores available for detection. However, simply increasing the probe concentration can be counterproductive and often produces marked changes in the probe's chemical and optical characteristics. The effective intracellular concentration of probes loaded by bulk permeabilization methods usually is much higher (>10-fold) than the extracellular incubation concentration. Additionally, the increased labeling of proteins or membranes ultimately leads to precipitation of the protein or gross changes in membrane permeability. Antibodies labeled with more than four to six fluorophores per protein may exhibit reduced specificity and reduced binding affinity. At high degrees of substitution, the extra fluorescence obtained per added fluorophore typically decreases due to self-quenching.
Compensation
Compensation is the mathematical process for correcting multiparameter flow cytometric data for spectral overlap. This overlap (“spillover”) results from the use of fluorescent dyes that are measurable in more than one detector; this spillover is correlated by a constant (“spillover coefficient”). The process of compensation is a simple application of linear algebra to correct for spillovers of all dyes into all detectors, such that on output, the data are effectively normalized so that each parameter contains information from a single dye.
Generally, the ability to process data is most effective when the visualization of data is presented without unnecessary correlations (i.e., when displaying graphs of one or two parameters there is no contribution of other (perhaps undisplayed) parameters to the distributions being shown). This becomes more problematic upon inclusion of two or more interacting parameters; the presence of multiple fluorescent signals must be accommodated within any fluorescence detection system for accurate quantification and analysis. Fluorescence is recorded using an emission filter chosen to collect the maximum amount of light coming from the fluorophore of interest and to exclude as much light as possible from other nearby fluorophores or fluorescent sources. While an emission filter efficiently captures the emission peak of the target fluorophore, it also may collect the light from one or more additional fluorophores due to spectral overlap in the emission profiles. Such data needs to compensated, i.e., a percentage of fluorescence is subtracted from one channel measuring a fluorophore and from a second channel measuring the fluorescence of the second (or multiple) fluorophore, such that the contribution of the incidental fluorescence is removed. Proper or correct compensation is achieved when the compensated data in each detector have no bias in the fluorescence distribution that is related to the intensity measured in any other detector.
Depending upon the instrument and software used, compensation may be set either in the instrument hardware before the sample is run or within the software after data collection. Every fluorophore combination that shows spectral overlap must be compensated.
Multicolor flow cytometry yields measurements of fluorescence from individual cells; however, biologists generally are interested in the amount of reagent bound to each individual cell. The process of transforming multicolor fluorescence measurements to yield estimates of the amounts of different dyes present is referred to as “fluorescence compensation”; To determine the amount of compensation required to correct the fluorescence data, single-color samples (either aliquots of the cell sample stained with each fluorophore separately or microspheres that capture an individual reagent) are utilized and analyzed in parallel with the experimental samples stained with multiple fluorophores.
Fluorescence Compensation
Although previous studies have attempted to measure two different dyes excited by the same laser but emitting at different wavelengths, it became apparent in such studies that each dye contributed some signal on each detector. It generally is believed that this was not a result of inadequacies in optical filters, but a fundamental limitation in the chemical physics of the dyes leading to emission of some light over a range of wavelengths broader than the peak emission zone. Although a FACS detector (channel) is intended to detect the light emitted by a given dye on a single cell, it also will detect light emitted by any other dye that is associated with the cell, excited by the same laser, and capable of emitting light at wavelengths that pass through the optical bandpass filter for the channel. The dyes, lasers and optical filters are chosen so that each detector is optimally sensitive to one dye, but, generally, each dye also will produce some signal on one or more other detectors. Therefore, to obtain an accurate estimate of the signal due to the dye of interest on a particular detector, it is necessary to evaluate the signal contributed by spectral overlap of other dyes and subtract that from the initial signal recorded by the detector.
Subsequently, studies focused on the constant ratio produced by the amount of signal produced by one dye on a detector intended for another dye, with the signal of the first dye on its own detector. This allowed for “compensated” outputs to be determined where the spectral overlap was adjusted; however these compensated outputs were proportional only to the amount of the dye of interest.
Two basic methods have been used to accomplish spectral overlap correction: analog compensation and computed compensation. Analog circuitry in the cytometer itself initially was used since fluorescence compensated data was needed for cell sorting with multiple dyes and the compensated data could be displayed to monitor data collection. However, if compensation settings were incorrect during the run, they could not be revised. Computed compensation starting with uncompensated measurements could be carried out after the fact and, if necessary, revised, but its use on cytometers themselves initially was limited by the available computing power.
Analog and computed compensation rely on the measurement of compensation control samples in order to specify the spectral overlap correction factors for each dye on each detector. The control samples consist of cells or particles stained separately with each of the dyes used in the experiment. A completely unstained sample also is useful usually. Analysis of data from these control samples yields spectral overlap factors between each dye and each detector, and the whole set of overlaps can be expressed as a spectral overlap matrix. Mathematically, fluorescence compensation is carried out by multiplying a vector consisting of the detector/color measurements for a particular cell by the inverse of the spectral overlap matrix (the “compensation matrix”) to obtain the calculated amount of each dye on that cell as a new vector.
The number of spectral overlaps to be evaluated and corrected increases rapidly with increasing numbers of dyes and detectors. For example, for N dyes on N detectors, there are N2 possible signal contributions, N of which represent each dye on its intended detector, and the other N2-N represent spectral overlaps. While two dyes give only two overlaps, ten dyes have 90 possible overlaps. Many of the overlaps are very small and can be ignored, but the number of relevant overlaps quickly goes beyond anything reasonable to set by hand, leading to a demand for computerized assistance in carrying out fluorescence compensation.
Compensation Errors
Correct compensation for more than two colors almost never can be achieved using the standard interface of adjusting compensation coefficients (rather than spillover coefficients), because of the interdependence of these values. Studies have reported that even properly compensated data may appear to be undercompensated. There are at least two distinct types of errors that contribute to imprecise compensation: errors arising from (1) photon-counting statistics, and (2) measurement errors. These are distinct in that the former are nonlinear, while the latter are linear. It generally is believed that it is not possible to properly set compensation by visual methods (i.e., relying on dot plots or histograms); nor is it possible to accurately analyze data using quadrant gates or control samples based on isotype controls in all channels. Importantly, this holds true irrespective of the use of newer digital electronics that obviate the use of log amplifiers (a significant source of measurement error).
A fundamental measurement error that never can be overcome is one arising from counting statistics. For most cytometry applications, the number of photoelectrons in the photomultiplier tube (PMT) detector is typically in the range of 1 to about 105, depending on the signal intensity. For example, autofluorescence in the fluorescein or phycoerythrin detectors (for lymphocytes) is typically below 10 photoelectrons. The error in this measurement must be at least as great as the counting error, which is the square-root of the count (i.e., 10±3.2 (±32%)). Even at 104 photoelectrons, which puts the signal into the third decade of fluorescence, the counting error is ±1%. These measurement errors contribute to the spread in compensated parameters.
Studies have reported that decreasing the number of photons (or photoelectrons at the first PMT dynode) has several effects on compensation. It has been reported that (1) reducing spillover decreases the “error” in the compensated distribution concomitantly; (2) as the number of photons available to the primary detector decreases, the error in the distribution increases concomitantly; (3) the spread downward occurs at a much lower intensity than the spread upwards (as is the case for proportionate errors); and (4) the spread upward occurs at a relative log-log slope of 1:2 because the photon-counting error is proportional to the square root of the measurement intensity (i.e., nonlinear). Thus, this visualization artifact cannot be corrected by overcompensating the data, because compensation is a linear process.
Compensation Error Correction
It generally is believed that no electronics can overcome the fundamental counting error inherent in measuring signal levels. This nonlinear error contribution in the data always will be present and lead to the spread of compensated data.
The degree to which the errors are apparent depends principally on two factors: the degree of spillover, and the brightness of the signal. Minimizing these errors can be accomplished by using fluorescent dyes that are as bright as possible, with as little spectral overlap as possible. Likewise, optimizing light collection will improve signal detection. If postacquistion compensation is necessary, then storing the data in as many channels as possible also may minimize error. However, the spreading of compensated data may continue to impact significantly the analysis and interpretation of data. For example, where this error is present, the use of linear “quadrant gates”, or any gate based on a completely unstained sample, would lead to erroneous results, since at higher intensities, the autofluorescence distribution will spread up into the “positive” gate. It generally is believed that the best control is to stain cells with all reagents except for the one of interest in order to determine the exact range of the negative population. This type of control may be termed “fluorescence minus one” (FMO). A nonlinear gate can be drawn based on an FMO control, and applied to the fully stained sample to determine which events are positive.
Some flow cytometry data analysis packages have offered software-assisted compensation based on positive-negative differences. The typical procedure has been for the user to apply gates to data on single stained control samples and to select cell populations that are positive and negative for each single dye to be used in multi-color staining. The software then computes the median (or mean) fluorescence for each population. The differences between the corresponding positive-negative population pairs in each data dimension are used to evaluate the elements of the spectral overlap matrix, and the compensation then can be applied to any cell sample stained with the appropriate dyes.
Some commercial software (such as, for example, FlowJo (Treestar, Inc., Ashland, Oreg.)) requires user interaction to analyze the compensation control samples and to identify appropriate gated populations but proceeds automatically from there. Other software (such as Diva (BD Biosciences)) includes partial automation of the positive-negative population difference method for evaluating the spectral overlap matrix in which a positive peak is found automatically and gated in a 1-dimensional fluorescence histogram of each compensation control sample after user-specified light scatter gating.
There are several problems with existing methodology for estimating fluorescence compensation. For example, existing methods rely only upon gated population means or medians to estimate the matrix coefficients. Further, most existing methods rely on subjective gating to exclude inappropriate events and to specify appropriate populations for evaluation of compensation coefficients. Additionally, existing methods of estimating coefficients rely on subjective human evaluation for validation of the quality of the resulting measurements, and fail to provide errors of estimates for the coefficients.
The described invention, which avoids many of the pitfalls of the current methods of determining fluorescence spectra overlap, provides a model-based approach to compensation that addresses these problems, uses compensation computation to define cocktails to minimize the effect of compensation, and uses criteria of availability to provide a rank-ordered list of reagent combinations. It provides a fully automatic method for computing slopes based on the use of all measurements taken for the sample; it does not rely on gating or other methods to distinguish signals from fluorescent versus non-fluorescent objects. Instead, the described invention utilizes all of the measurements to compute the required slopes. Further, while current methods do not enable computation of the accuracy of resultant slopes, the described invention automatically provides quality metrics that report whether the slopes computed for any of the fluorescence spectra are accurate enough to be useful over the necessary dynamic range. Additionally, while current methods do not readily provide a way to compute fluorescence overlap corrections for measurements in which full fluorescence spectra are taken for each item (as opposed to a limited series of measurements of “peak channel” fluorescence), the described invention is fully applicable with full fluorescence spectra measurements. Furthermore, the described invention provides a method for determining the absolute detector sensitivity in terms of number of photons. This allows for an upper bound on the quality of signal that can be obtained.
The described invention also provides personalizable, customizable and automatable methods to replace the arduous and sometime intractable methods used currently in flow cytometry and other multiparameter assays. These assays, which reveal markers (target molecules) co-expressed in or on cells or particles of interest, are widely used in research and medicine to discriminate the various types of cells present in blood and other organs. However, they have yet to achieve their full potential, largely because the complex knowledge and functions skills needed to perform the assays effectively restricts their current use to large medical centers and research institutions and deters development of new assays that could provide even greater benefit.
To overcome these restrictions, the invention substitutes knowledge-based and knowledgeable computer technology that simplifies the application and extension of flow cytometry and other fluorescence-based assays and hence makes these assays more accessible to the laboratories whose personnel are only modestly skilled in developing and applying them. Thus, the invention provides a series of interconnected software utilities that facilitate the various assay steps as they are performed. These range from early stage utilities that provide help with protocol design tasks such as the acquisition, selection and optimization of reagent combinations (stain sets) to late stage utilities that enable transfer of the protocol information needed during data collection to annotate data and apply automated fluorescence compensation to initiate data analysis.
The invention provides extensive help for protocol design. Using built in, supplied or extracted knowledge about marker expression, reagent specificity, fluorescence spectra and compensation, instrument detection capabilities and other factors, the invention can list all possible reagent combinations (stain sets) constructible from available reagents. Furthermore, it can rank the stain sets according to predicted optimal efficiency for detecting individual markers or cell types (for example, by evaluating likely interference due to fluorescence overlap among reagents detecting markers on the same target cell). In essence, the invention allows users to indicate the markers (e.g., cell surface determinants) that they want the stain set to detect and to specify expected levels of marker expression on cells that will be stained. It then returns the possible reagent combinations that could accomplish this task and indicates which stains sets are likely to be most efficient for this purpose.
To further release users from difficult and tedious tasks, the invention allows users to point-and-click or drag-and-drop to select and re-use reagents, subjects, samples, keywords and other assay items. In this way, it provides users with the tools to create and print full, executable assay protocols and to store these in machine readable format. To further facilitate selections, the invention displays reagents and other assay items in unique, highly flexible and personalizable tree-table formats that efficiently communicate necessary knowledge. In addition, the invention helps users to transfer protocol information to data collection instruments and to add the information to data files and enables archiving and long-term maintenance of well-annotated archived data. To help users manage local reagent supplies and select and purchase additional reagents, the invention provides an automated “personal reagent shopper” that can maintain and search catalogs of commercial reagents to locate and import reagents that are compatible with detection instrument capabilities and with reagents that have already been selected into the stain set. In addition, to help users avoid costly but common omissions of key assay controls, the invention automates the specification and inclusion of control samples necessary for fluorescence compensation computations and for analysis of data from samples that show minimal staining with key reagents on key subsets.
Thus, with these and other automated capabilities described herein, the invention provides for performance of high quality multiparameter flow cytometry and other fluorescence-based assays that currently only are accessible to practitioners who acquire the knowledge necessary to master the intricacies of the technology and acquire the knowledge to perform these assays well. Since such assays are becoming increasingly more important in medical practice, and since they are crucial to the development of cancer therapies, stem cell transplantation methods and modern approaches to the treatment of infectious diseases, the invention has a strong practical significance that couples well with and in addition to opening key methods for using computer technology to the advances it brings to the ways computer technology can used to break down barriers to optimal use of powerful biomedical instrumentation.